To create an accuracy predictor, we build an Architecture Library of trained models sampled from the search space, see Figure REF (b). These models can be trained from scratch or finetuned quickly using weight initialization from BKD (Section REF ).
Subsequently, we fit a linear regression model, typically using second-order terms, to predict the full search space's accuracy using the quality metrics stored in the Block Library as features and the accuracy from the Architecture Library as targets. Figure REF (left) shows that the linear predictor fits well with a test-set of network architectures trained on ImageNet [1]} in the DONNA space (MSE=0.2, KT [2]}=0.91). This predictor can be understood as a sensitivity model that indicates which blocks should be large, and which ones can be small, to build networks with high accuracy. Appendix REF  discusses the effectiveness of different derived quality metrics on the quality of the accuracy prediction.
